Langchain-Chatchat/tests/test_qwen_agent.py

import sys
from pathlib import Path
sys.path.append(str(Path(__file__).parent.parent))

import asyncio
import json
from pprint import pprint
from langchain.agents import AgentExecutor
from langchain_openai.chat_models import ChatOpenAI
# from langchain.chat_models.openai import ChatOpenAI
from server.agent.tools_factory.tools_registry import all_tools
from server.agent.agent_factory.qwen_agent import create_structured_qwen_chat_agent
from server.callback_handler.agent_callback_handler import AgentExecutorAsyncIteratorCallbackHandler
from langchain import globals

# globals.set_debug(True)
# globals.set_verbose(True)


async def test1():
    callback = AgentExecutorAsyncIteratorCallbackHandler()
    tools = [t.copy(update={"callbacks": [callback]}) for t in all_tools]
    # qwen_model = get_ChatOpenAI("Qwen-1_8B-Chat", 0.01, streaming=True, callbacks=[callback])
    qwen_model = ChatOpenAI(base_url="http://127.0.0.1:9997/v1",
                            api_key="empty",
                            streaming=False,
                            temperature=0.01,
                            model="qwen",
                            callbacks=[callback],
                            )
    agent = create_structured_qwen_chat_agent(tools=tools, llm=qwen_model)
    executor = AgentExecutor(agent=agent, tools=tools, verbose=True, callbacks=[callback])

    # ret = executor.invoke("苏州今天冷吗")
    ret = asyncio.create_task(executor.ainvoke({"input": "苏州今天冷吗"}))
    async for chunk in callback.aiter():
        print(chunk)
    # ret = executor.invoke("从知识库samples中查询chatchat项目简介")
    # ret = executor.invoke("chatchat项目主要issue有哪些")
    await ret


async def test_server_chat():
    from server.chat.chat import chat

    mc={'preprocess_model': {
        'qwen': {
            'temperature': 0.4,
            'max_tokens': 2048,
            'history_len': 100,
            'prompt_name': 'default',
            'callbacks': False}
        },
        'llm_model': {
            'qwen': {
                'temperature': 0.9,
                'max_tokens': 4096,
                'history_len': 3,
                'prompt_name': 'default',
                'callbacks': True}
            },
        'action_model': {
            'qwen': {
                'temperature': 0.01,
                'max_tokens': 4096,
                'prompt_name': 'qwen',
                'callbacks': True}
            },
        'postprocess_model': {
            'qwen': {
                'temperature': 0.01,
                'max_tokens': 4096,
                'prompt_name': 'default',
                'callbacks': True}
            }
        }

    tc={'weather_check': {'use': False, 'api-key': 'your key'}}

    async for x in (await chat("苏州天气如何",{},
                            model_config=mc,
                            tool_config=tc,
                            conversation_id=None,
                            history_len=-1,
                            history=[],
                            stream=True)).body_iterator:
        pprint(x)


async def test_text2image():
    from server.chat.chat import chat

    mc={'preprocess_model': {
        'qwen-api': {
            'temperature': 0.4,
            'max_tokens': 2048,
            'history_len': 100,
            'prompt_name': 'default',
            'callbacks': False}
        },
        'llm_model': {
            'qwen-api': {
                'temperature': 0.9,
                'max_tokens': 4096,
                'history_len': 3,
                'prompt_name': 'default',
                'callbacks': True}
            },
        'action_model': {
            'qwen-api': {
                'temperature': 0.01,
                'max_tokens': 4096,
                'prompt_name': 'qwen',
                'callbacks': True}
            },
        'postprocess_model': {
            'qwen-api': {
                'temperature': 0.01,
                'max_tokens': 4096,
                'prompt_name': 'default',
                'callbacks': True}
            },
        'image_model': {
            'sd-turbo': {}
        }
    }

    tc={'text2images': {'use': True}}

    async for x in (await chat("draw a house",{},
                            model_config=mc,
                            tool_config=tc,
                            conversation_id=None,
                            history_len=-1,
                            history=[],
                            stream=False)).body_iterator:
        x = json.loads(x)
        pprint(x)

asyncio.run(test_text2image())